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I think the crucial point here is what you precisely mean by mislabelled. Google's image classifier will likely do a 'pretty good' job of retrieving images with the given subject included, but how strict or lenient your class requisites are is quite important. For example, if one of your classes is 'dog' there may be hundreds of images procured from scraping ...


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The quick answer: yes you can, just add images without labels, just make sure that in the negative samples there are no cars or you will make the AI crazy (i.e. convergence & instability issues). However that might not be the better approach to go. Why? Because your dataset already have enough negative examples. This was pointed out by the famous paper ...


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It depends on what is your ultimate goal. If your goal is to simply classify the object in the image, having more complex output won't help. Simpler output representation yields better result. If your goal is to detect the bounding box, output the bounding box. There is no need for a more complex output feature. If you use a segmentation method for bounding ...


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Ground Truth 'Ground truth' is that data or information that you have that is 'true' or assumed to be true. That means that you have high or perfect knowledge of what it is. For example, in your image of numbers, you know that the first row are zeros, the second row are ones, the third are twos, and so on. You have 10 rows of data, each row is of a ...


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Padding is indeed the easiest solution. And if no bias is used then masking the extra values during the loss computation is also not necessary, since it's enough to use zero as padding value. You might be interested though in checking Spatial Pyramid Pooling. This pooling method allows to combine fully convolutional modules and dense layers, i.e, it can be ...


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One thing to try first is Focal Loss. This particular loss works well for classification or object detection where your dataset is unbalanced and contains many classes. In short, the loss suppresses highly confident predictions and gives the model more room to learn from other less confident classes. You can read this blog to have more intuition about focal ...


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The good folks behind Spacy have their paid product called Prodigy which is a data labeling tool. I haven't used it but it appears you can host it somewhere and then you would just have to send the link to the students. It is a little pricey but you get a lifetime license... A free alternative might be Label Studio but I am not sure how easy it is to host it ...


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These two terms could easily refer to the same thing, depending on the context. For example, a lazy person could easily say something like this We compute the loss/error between the prediction (of the model) and the ground truth. Here, the ground-truth refers to the "officially correct" label (categorical or numerical) for a given input with ...


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In my opinion, the second option will be more general. You can refer to some famous datasets for object detection task such as COCO or Pascal VOC, they usually accept the intersect annotations. As the image below, image from this link where they process the annotation of COCO dataset. I think the reason is that the model will be easier to separate the ...


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You can reduce your photo size and scale the corresponding boxes to the new dimensions (416x416). Or if you want to go with your technique, you can slice the image and then, check if the bounding box lies in the slice, then, reorient it according to the slice you took. Take a look at albumentations library for this.


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Yes, you should label it the same. But more importantly you need to make sure that each perturbation of the image doesn't change some important character of the image. Consider training an apple classifier. If you plan to augment data by altering the RGB values, you need to be wary that you might cause issues in classification tasks where color is ...


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I will break it down for you in very simple words. The accuracy will drop down as you label them wrong. In simpler words- accuracy is directly proportional on how perfect the data is labelled. If you think about it, suppose you have 2 categories-cats and dogs, and you have a dataset of 10,000 pictures. Out of which 50 are wrongly labelled. The accuracy will ...


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